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Stock trading & prediction using deep learning neural networks

Author

Gowri, Kannan

Date of Issue

2017

School

School of Electrical and Electronic Engineering

Abstract

This report describes various deep neural network models based on technical analysis to predict stock prices. Three stocks – Walmart, HSBC and Petrobras are chosen to test Multilayer Perceptron, Deep Belief Network (DBN), Convolutional Neural Network (CNN) and Random Forests. A hybrid model comprising of a DBN with a NN stacked on top is proposed in this report. 25% of the data is used for testing while the rest is used for training and validation of models. The models are tested for a wide range of parameters including activation functions, number of hidden layers, 5 day vs 1 day forecast, 20, 10 and 5 day prior input (batch size), epochs, learning rate and momentum. The prediction results for the model with the optimal set of parameters are compared against a published paper’s work on Multilayer Perceptron model that used the same input data sets.